Semantic Segmentation And Instance Segmentation Of Vehicles Based On Convolutional Neural Networks

Two new and difficult tasks in the field of computer vision in recent years have been the separation of image semantics and instance segmentation.With the popularity of neural network algorithms in deep learning,these two tasks have become widespread and important in practical applications.In the aspect of intelligent transportation,fast and accurate segmentation of vehicles in traffic scenes is one of the key research techniques in intelligent transportation systems,so it is important to study the technology of vehicle semantic segmentation and instance segmentation in environmentally variable traffic scenarios.The practical significance.As computer vision enters the era of deep learning,the difficulties of image semantic segmentation and instance segmentation are gradually being overcome.Researchers have proposed a series of methods based on convolutional neural network for semantic segmentation and instance segmentation.This paper mainly focuses on the improvement of the convolutional neural network model by vehicle semantic segmentation and the construction of the network model for the instance segmentation of vehicles.main tasks as follows:(1)The detailed analysis introduces the PASCAL VOC2012 dataset and the CITYSCAPES dataset.For the problem of insufficient vehicle semantic segmentation dataset,the dataset for semantic segmentation,namely the VEHICLEDATASETt dataset,is built to expand the dataset of the semantic segmentation of the vehicle.(2)In this paper,the problem of complex network and slow training speed for the semantic segmentation of vehicles is optimized to simplify the full convolutional neural network,and the target segmentation task is transformed into a pixel-based binary classification problem.The optimized convolutional neural network is trained using the expanded data set,and the problem of the complex complexity of the network is also compared under the different types of classification.The accuracy of network segmentation is increased with appropriate reduction of network complexity.The optimization method also includes changing the random gradient descent method originally used by the network,and choosing to use the Adam optimization function to train the network to improve the training speed of the network.Reduce the time cost of network training.(3)In the aspect of segmentation of vehicle instances,an example segmentation network model combining mask map and class activation map generated by semantic segmentation network is proposed to realize segmentation of vehicle instances in trafficenvironment.The network model of image semantic segmentation has this feature built on the classification network model,but the mask map of semantic segmentation can only segment the target from the original image,but can not distinguish the instance target of the same class.The class activation technology can be directly constructed on the classification network.Using the class activation technology to extract the feature map from the convolutional neural network to the generated heat map,the position of different instances on the original image can be located to realize the instance differentiation of the image.This combines the information of the mask map information and the class activation map to implement an instance segmentation of the vehicle.Finally,through experimental verification,the ideal experimental results were obtained.